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    "\n",
    "# 🧠 Multi-Product Competitor Intelligence Summarizer using Web Scraping + LLM\n",
    "\n",
    "This notebook scrapes product pages using `Selenium`, collects the product information, and summarizes key features and comparison insights using `Ollama (LLaMA3) and OpenAI`.\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "7b87cadb-d513-4303-baee-a37b6f938e4d",
   "metadata": {},
   "outputs": [],
   "source": [
    "# imports\n",
    "\n",
    "import os\n",
    "import requests\n",
    "from dotenv import load_dotenv\n",
    "from openai import OpenAI\n",
    "\n",
    "# Load environment variables in a file called .env\n",
    "\n",
    "load_dotenv(override=True)\n",
    "api_key = os.getenv('OPENAI_API_KEY')\n",
    "\n",
    "# Check the key\n",
    "\n",
    "if not api_key:\n",
    "    print(\"No API key was found - please head over to the troubleshooting notebook in this folder to identify & fix!\")\n",
    "elif not api_key.startswith(\"sk-proj-\"):\n",
    "    print(\"An API key was found, but it doesn't start sk-proj-; please check you're using the right key - see troubleshooting notebook\")\n",
    "elif api_key.strip() != api_key:\n",
    "    print(\"An API key was found, but it looks like it might have space or tab characters at the start or end - please remove them - see troubleshooting notebook\")\n",
    "else:\n",
    "    print(\"API key found and looks good so far!\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "abdb8417-c5dc-44bc-9bee-2e059d162699",
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   "source": [
    "# Define our system prompt - you can experiment with this later, changing the last sentence to 'Respond in markdown in Spanish.\"\n",
    "\n",
    "system_prompt = \"Summarize the following product information for comparison.\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "38245e18",
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "# 📦 Install required packages (run once)\n",
    "!pip install selenium bs4 requests\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "88ae528b-aefe-4c64-b927-676e739194af",
   "metadata": {},
   "outputs": [],
   "source": [
    "openai = OpenAI()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d4a831a5",
   "metadata": {},
   "outputs": [],
   "source": [
    "def summarize_with_openai(text, model=\"gpt-4o-mini\"):\n",
    "    response = openai.chat.completions.create(\n",
    "        model=model,\n",
    "        messages=[\n",
    "            {\"role\": \"system\", \"content\": system_prompt},\n",
    "            {\"role\": \"user\", \"content\": text}\n",
    "        ],\n",
    "        temperature=0.7\n",
    "    )\n",
    "    return response.choices[0].message.content\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ef65cd72",
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "# ⚙️ Selenium setup (headless)\n",
    "from selenium import webdriver\n",
    "from selenium.webdriver.chrome.options import Options\n",
    "from selenium.webdriver.common.by import By\n",
    "import time\n",
    "\n",
    "def scrape_text_from_url(url):\n",
    "    options = Options()\n",
    "    options.add_argument(\"--headless=new\")\n",
    "    driver = webdriver.Chrome(options=options)\n",
    "    driver.get(url)\n",
    "    time.sleep(3)\n",
    "    \n",
    "    # You can tune this selector depending on the site\n",
    "    body = driver.find_element(By.TAG_NAME, 'body')\n",
    "    text = body.text\n",
    "    driver.quit()\n",
    "    return text.strip()\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "36e19014",
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "# 🧠 LLM Prompting using Ollama (local llama3)\n",
    "import subprocess\n",
    "\n",
    "def summarize_with_ollama(text):\n",
    "    prompt = f\"Summarize the following product description:\\n\\n{text}\\n\\nSummary:\"\n",
    "    try:\n",
    "        print(\"inside ollama\")\n",
    "        result = subprocess.run(\n",
    "            [\"ollama\", \"run\", \"llama3.2\"],\n",
    "            input=prompt,\n",
    "            capture_output=True, text=True, check=True, encoding=\"utf-8\"\n",
    "        )\n",
    "        print(\"git result\")\n",
    "        return result.stdout.strip()\n",
    "    except subprocess.CalledProcessError as e:\n",
    "        return f\"Error running ollama: {e.stderr}\"\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e04cea6e",
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "# 🔁 Analyze multiple product URLs and summarize\n",
    "product_urls = {\n",
    "    \"iPhone 15 Pro\": \"https://www.apple.com/in/iphone-15-pro/\",\n",
    "    \"Samsung S24 Ultra\": \"https://www.samsung.com/in/smartphones/galaxy-s24-ultra/\",\n",
    "}\n",
    "\n",
    "product_texts = {}\n",
    "\n",
    "for name, url in product_urls.items():\n",
    "    print(f\"Scraping {name} ...\")\n",
    "    product_texts[name] = scrape_text_from_url(url)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "5ebd5a20",
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "# 📄 Display side-by-side summaries\n",
    "for name, text in product_texts.items():\n",
    "    print(f\"\\n🔹 {name} Summary with Ollama:\")\n",
    "    print(summarize_with_ollama(text))\n",
    "\n",
    "    print(f\"\\n🔹 {name} Summary with OpenAI GPT:\")\n",
    "    print(summarize_with_openai(text))\n",
    "    print(\"=\"*100)\n",
    "\n"
   ]
  },
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